We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques.

翻译：我们处理如何在建议者系统中强有力地解释自然语言改进(或批评)的问题。特别是在人文建议环境中,人们经常使用软属性来表达对项目的偏好,包括电影原创性、地点的灵敏性或食谱的复杂性等概念。虽然在建议者系统中广泛研究了二进制标记,软属性往往涉及主观和背景方面,无法以这种方式可靠地捕捉,也不能在知识库中作为客观的二进制真理。这在衡量软属性排名时也增加了重要的考虑因素。我们建议以个性化相对说明而不是绝对的项目属性来更自然地表达。我们提出了新的数据收集技术和评估方法,以及新的公共数据集。我们还提出了一套评分方法,从不受监督到监管薄弱到充分监督,作为解释和根据基于批评的软属性采取行动的一个步骤。